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Cloud Video System Availability Assessment Using Markov and Semi-Markov Models 16

Cloud Video System Availability Assessment Using Markov and ...

16th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2022

作者： Ivanchenko, Oleg Kharchenko, Vyacheslav Udovyk, Iryna Ponochovnyi, Yuriy Dnipro University of Technology Dept. of Computer Systems and Software Dnipro Ukraine National Aerospace University Dept. of Computer Systems Networks and Cybersecurity Kharkiv Aviation Institute Kharkiv Ukraine Dept. of Information Systems and Technologies Poltava Ukraine

ISBN: (纸本)9781665468619

Comparative analysis of Markovian and Semi-Markovian modelling for cloud video system availability is performed considering its architecture and dependability characteristics of various components. Initial data for modelling is based on review of research and operation data of cloud systems. The results of modelling provide decision making related to choice of technique of modelling and availability assessment. Besides, they contributes to ensuring the high availability of cloud systems and can be used by developers in order to address the issues into DevOps engineering area. © 2022 IEEE.

关键词： Availability

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Rare Collision Risk Estimation of Autonomous Vehicles with Multi-Agent Situation Awareness

Rare Collision Risk Estimation of Autonomous Vehicles with M...

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International Conference on Intelligent Transportation

作者： Mahdieh Zaker Henk A.P. Blom Sadegh Soudjani Abolfazl Lavaei School of Computing Newcastle University United Kingdom Delft University of Technology The Netherlands Max Planck Institute for Software Systems Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

This paper offers a formal framework for the rare collision risk estimation of autonomous vehicles (AVs) with multi-agent situation awareness, affected by different sources of noise in a complex dynamic environment. The estimation framework consists of two complementary parts: modeling formalism and a rare event estimation method using sequential Monte Carlo (MC) simulation instead of importance sampling. By defining incremental levels of severity that must be passed before a collision, a sequence of MC simulations can be applied from one level to the next. This particular sequential MC method consists of the simulation of an Interacting Particle System (IPS) in combination with Fixed Assignment Splitting (FAS) of particles that reach the next level. We model AVs equipped with the situation awareness as general stochastic hybrid systems (GSHS), including the IPS-FAS relevant severity levels, and assess the probability of collision in a lane-change scenario where two self-driving vehicles simultaneously intend to switch lanes into a shared one while utilizing the time-to-collision measure for decision-making as required. The IPS-FAS method is subsequently used to estimate collision risk for this GSHS model of the lane-changing scenario. The results show that in contrast to straightforward MC simulation, IPS-FAS is able to quantify the very low collision risk for the scenario of interest.

关键词： Monte Carlo methods Noise Decision making Estimation Stochastic processes Switches IP networks Vehicle dynamics Autonomous vehicles Intelligent transportation systems

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ODRL-Based Resource Definition in Business Processes

ODRL-Based Resource Definition in Business Processes

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18th International Conference on software Technologies, ICSOFT 2023

作者： Maamar, Zakaria Benna, Amel Li, Minglin Huang, Huiru Xu, Yang College of Computing and IT University of Doha for Science and Technology Doha Qatar Department of Multimedia and Information Systems CERIST Algiers Algeria School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9789897586651

Despite the popularity of Resource-as-a-Service (RaaS) model, it falls short of providing low-level, flexible control over resources in terms of which category of users can use them, when and where users can use them, and how much users need to pay for them. To provide such a control, this paper presents 2 intermediary models between resources and services referred to as Resource-as-an-Asset (RaaA) and Asset-as-a-Service (AaaS), respectively. First, resource-related constructs are mapped onto asset-related constructs and then, the same asset-related constructs are mapped onto service-related constructs. A projection operator is developed to support construct mappings based on a set of pre-defined rules. To illustrate asset-related constructs, the paper resorts to the Open Digital Rights Language (ODRL) defining what is permitted, forbidden, or obliged over an asset. A system implementing construct mappings is presented in the paper as well. © 2023 by SCITEPRESS-Science and technology Publications, Lda.

关键词： Mapping

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End-Users vs software Practitioners: Recruitment Challenges and Strategies in software Engineering Research

End-Users vs Software Practitioners: Recruitment Challenges ...

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IEEE Symposium on Visual Languages and Human Centric computing (VL/HCC)

作者： Wei Wang Dulaji Hidellaarachchi John Grundy Hourieh Khalajzadeh Humphrey O. Obie Anuradha Madugalla Dept of Software Systems and Cybersecurity Monash University Australia School of Information Technology Deakin University Melbourne Australia

ISBN: (数字)9798350366136

ISBN: (纸本)9798350376920

This paper shares insights from our first-hand experience with key recruitment challenges encountered in software engineering research, focusing on two distinct participant groups: end-users and software practitioners. By conducting a reflective analysis, we emphasise the particular challenges we faced when engaging these groups during empirical study recruitment phases. Significant challenges we faced in recruiting end-users include ensuring authenticity, maintaining engagement, achieving demographic diversity, and addressing privacy concerns. Conversely, we faced different challenges when recruiting software practitioners, including sourcing the right expertise, utilising online recruiting platforms, navigating time constraints, aligning incentives, obtaining a representative sample, and coordinating with remote and distributed teams. By detailing the strategies we employed to address these challenges, this paper contributes practical knowledge to enhance the efficacy and inclusiveness of research practices, ultimately fostering more robust software engineering research outcomes.

关键词： Knowledge engineering Visualization Privacy Navigation Focusing software software reliability Time factors Recruitment software engineering

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Rare Collision Risk Estimation of Autonomous Vehicles with Multi-Agent Situation Awareness

arXiv

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arXiv 2024年

作者： Zaker, Mahdieh Blom, Henk A.P. Soudjani, Sadegh Lavaei, Abolfazl School of Computing Newcastle University United Kingdom Delft University of Technology Netherlands Max Planck Institute For Software Systems Germany

This paper offers a formal framework for the rare collision risk estimation of autonomous vehicles (AVs) with multi-agent situation awareness, affected by different sources of noise in a complex dynamic environment. In our proposed setting, the situation awareness is considered for one of the ego vehicles by aggregating a range of diverse information gathered from other vehicles into a vector. We model AVs equipped with the situation awareness as general stochastic hybrid systems (GSHS) and assess the probability of collision in a lane-change scenario where two self-driving vehicles simultaneously intend to switch lanes into a shared one, while utilizing the time-to-collision measure for decision-making as required. Due to the substantial data requirements of simulation-based methods for the rare collision risk estimation, we leverage a multi-level importance splitting technique, known as interacting particle system-based estimation with fixed assignment splitting (IPS-FAS). This approach allows us to estimate the probability of a rare event by employing a group of interacting particles. Specifically, each particle embodies a system trajectory and engages with others through resampling and branching, focusing computational resources on trajectories with the highest probability of encountering the rare event. The effectiveness of our proposed approach is demonstrated through an extensive simulation of a lane-change scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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AltRecSys: A Workshop on Alternative, Unexpected, and Critical Ideas in Recommendation 24

AltRecSys: A Workshop on Alternative, Unexpected, and Critic...

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Proceedings of the 18th ACM Conference on Recommender systems

作者： Michael Ekstrand Maria Soledad Pera Alan Said Information Science Drexel University USA Web Information Systems - Software Technology TU Delft Netherlands Applied IT University of Gothenburg Sweden

ISBN: (纸本)9798400705052

The AltRecsys workshop, held in conjunction with the 18th edition of the ACM Conference on Recommender systems (RecSys) in Bari, Italy, provides a platform for highlighting “alternative” work in recommender systems. Modeled after *** and the CRAFT sessions at the FAccT conference, AltRecSys offers a space to discuss interesting, preliminary, offbeat, unexpected, and critical ideas in recommender systems that do not (yet) fit well into the kinds of publications and formats for the main conference or traditional workshops. This workshop is not a venue to showcase research advances. Instead, it is envisioned as a forum where researchers, (industry) practitioners, and other associated stakeholders can exchange ideas and together identify areas of study and new questions to expand the discussions and research agendas of the RecSys community in future years. The call for contributions and the workshop sessions are centered around the question “what are the vital questions, needs, or opportunities that the RecSys community is currently overlooking?”

关键词： alternative ideas

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Analyzing Deep Learning Techniques in Natural Scene Image Classification

Analyzing Deep Learning Techniques in Natural Scene Image Cl...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Kanavos, Athanasios Papadimitriou, Orestis Al-Hussaeni, Khalil Karamitsos, Ioannis Maragoudakis, Manolis University of the Aegean Department of Information and Communication Systems Engineering Samos Greece Rochester Institute of Technology Computing Sciences Department Dubai United Arab Emirates Rochester Institute of Technology Graduate and Research Department Dubai United Arab Emirates Ionian University Department of Informatics Corfu Greece

ISBN: (纸本)9798350362480

Image classification is a fundamental task in computer vision, with wide applications including autonomous navigation, content recommendation, and environmental monitoring. This paper offers a comprehensive comparative analysis of deep learning techniques using the Intel Natural Scenes Image dataset, which features diverse scenes such as forests, mountains, seas, streets, buildings, and glaciers. Our study evaluates the performance of various Convolutional Neural Network (CNN) architectures, focusing on aspects such as network design, hyperparameters, data augmentation, and transfer learning strategies. We describe our experimental setup in detail, including the CNN models used, preprocessing techniques applied, and evaluation metrics employed. The results and discussions present key findings, highlighting the strengths and limitations of the approaches studied and providing guidance for future research and practical applications. Our systematic analysis yields valuable insights into effective strategies for recognizing natural scenes. © 2024 IEEE.

关键词： Convolutional neural networks

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ASDF: A Differential Testing Framework for Automatic Speech Recognition systems

ASDF: A Differential Testing Framework for Automatic Speech ...

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International Conference on software Testing, Verification, and Validation, ICST

作者： Daniel Hao Xian Yuen Andrew Yong Chen Pang Zhou Yang Chun Yong Chong Mei Kuan Lim David Lo School of Information Technology Monash University Malaysia School of Computing and Information Systems Management University Singapore

Recent years have witnessed wider adoption of Automated Speech Recognition (ASR) techniques in various domains. Consequently, evaluating and enhancing the quality of ASR systems is of great importance. This paper proposes Asdf, an Automated Speech Recognition Differential Testing Framework to test ASR systems. Asdf extends an existing ASR testing tool, the CrossASR++, which synthesizes test cases from a text corpus. However, CrossASR++ fails to make use of the text corpus efficiently and provides limited information on how the failed test cases can improve ASR systems. To address these limitations, our tool incorporates two novel features: (1) a text transformation module to boost the number of generated test cases and uncover more errors in ASR systems, and (2) a phonetic analysis module to identify phonemes that the ASR systems tend to transcribe incorrectly. Asdf generates more high-quality test cases by applying various text transformation methods (e.g., changing tense) to the input text in a failed test case. By doing so, Asdf can utilize a small text corpus to generate a large number of audio test cases, something which CrossASR++ is not capable of. In addition, Asdf implements more metrics to evaluate the performance of ASR systems from multiple perspectives. Asdf performs phonetic analysis on the identified failed test cases to identify the phonemes that ASR systems tend to transcribe incorrectly, providing useful information for developers to improve ASR systems. The demonstration video of our tool is made online at https://***/watch?v=DzVwfc3h9As. The implementation is available at https://***/danielyuenhx/asdf-differential-testing.

关键词： software testing Measurement Phonetics Automatic speech recognition

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Optimized Deep Learning Mechanism for Intrusion Detection: Leveraging RFE-Based Feature Selection and PCA for Improved Accuracy

Optimized Deep Learning Mechanism for Intrusion Detection: L...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： M. Rajkumar Lakshi VS Karthik R Pavithra S Department of Smart Computing School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India B. Tech-Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

With the growing demand for wireless networks, network traffic has surged, particularly involving large-scale data transmission. This results in an increased number of network security threats and the emergence of various cyber-attacks. To overcome these threats, the development of an efficient intrusion detection system (IDS) has become crucial. By leveraging the recent advancements in Artificial intelligence (AI), this project introduces an optimized deep learning mechanism utilizing a prevalent deep learning model, the Deep Neural Network (DNN) algorithm. This proposed method uses feature engineering techniques like feature selection and feature reduction. Given the high dimensionality of the selected datasets, these techniques are employed to prevent over-fitting. Recursive feature elimination performs feature selection followed by Principal Component Analysis for the feature reduction process, thus enhancing the model's efficiency. The system is assessed using two benchmark datasets: KDD and UNSW-NB15. The experimental results indicate that the presented DNN model on the KDD dataset has achieved 92.45% accuracy and 91.35% accuracy on the UNSW-NB15 dataset for intrusion classification.

关键词： Deep learning Accuracy Wireless networks Computational modeling Intrusion detection Artificial neural networks Feature extraction Data models Tuning Principal component analysis

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Exploring and Repairing Gender Fairness Violations in Word Embedding-based Sentiment Analysis Model through Adversarial Patches

Exploring and Repairing Gender Fairness Violations in Word E...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Lin Sze Khoo Jia Qi Bay Ming Lee Kimberly Yap Mei Kuan Lim Chun Yong Chong Zhou Yang David Lo School of Information Technology Monash University Malaysia School of Computing and Information Systems Singapore Management University

With the advancement of sentiment analysis (SA) models and their incorporation into our daily lives, fairness testing on these models is crucial, since unfair decisions can cause discrimination to a large population. Nevertheless, some challenges in fairness testing include the unknown oracle, the difficulty in generating suitable test inputs, and the lack of a reliable way of fixing the issues. To fill in these gaps, BiasRV, a tool based on metamorphic testing (MT), was introduced and succeeded in uncovering fairness issues in a transformer-based model. However, the extent of unfairness in other SA models has not been thoroughly investigated. Our work conducts a more comprehensive empirical study to reveal the extent of fairness violations, specifically gender fairness, exhibited by other popular word embedding-based SA models. We define fairness violation as the behavior in which an SA model predicts variants created from a text, which merely differ in gender classes, to have different sentiments. Our inspection utilizing BiasRV uncovers at least 30 fairness violations (at BiasRV’s default threshold) in all three SA models. Realizing the importance of addressing such significant violations, we introduce adversarial patches (AP) as a way of patch generation in an automated program repair (APR) system to fix them. We adopt adversarial fine-tuning in AP by retraining SA models using adversarial examples, which are bias-uncovering test cases dynamically generated by a tool named BiasFinder at runtime. Evaluation of the SA models shows that our proposed AP reduces fairness violations by at least 25%.

关键词： Analytical models Sentiment analysis Runtime Sociology Predictive models Transformers software

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